The long-term goal of this program is to improve patient care by optimizing and validating quantitative magnetic resonance imaging methods for the early prediction of brain cancer response to therapy. Currently, contrast-enhanced MRI (CE-MRI) represents the standard for guiding almost all aspects of brain tumor clinical management, including surgical biopsy/resection, radiation treatment planning, and post-treatment surveillance for response assessment. Unfortunately, CE-MRI?s accuracy remains limited, which creates significant clinical challenges. Thus, clinical decisions often require surgical biopsy for definitive diagnosis, which increases medical costs, patient morbidity/mortality, and resource utilization. To overcome the limitations of CE-MRI, dynamic susceptibility contrast (DSC) MRI and dynamic contrast enhanced (DCE) MRI are increasingly used to evaluate tumor perfusion and permeability. Studies have shown that DSC/DCE parameters correlate with tumor grade, can predict the likelihood of tumor progression after therapy, and differentiate treatment related effects versus tumor progression. However, the widespread clinical adoption and incorporation of DSC-MRI into multi-site clinical trials has been hindered due to variable acquisition methods, contrast agent dosing schemes and analysis protocols, which to date, have yet to be standardized and automated for clinical use. These issues are known to affect the repeatability and interpretation of DSC-MRI metrics. Spin and gradient echo (SAGE) DSC-MRI sequences enable the use of lower doses of Gd-based contrast agents, require less scan time, are less sensitive to acquisition parameters, are methodologically more reproducible, yield more accurate perfusion parameters, provide simultaneous measures of DCE-MRI, vessel size and vessel architectural imaging data, oxygen delivery and novel metrics highly sensitive to tumor cellular characteristics. Accordingly, SAGE methods enable the interrogation of unique and complementary readouts on tumor microstructure and function that correlate with clinical outcomes and can identify patients responding to therapy. Before clinical trials can benefit from SAGE based DSC-MRI the acquisition and analysis protocols need to be optimized, automated and standardized. Thus, we propose to: 1) implement multi-vendor, SAGE- DSC-MRI protocols, 2) establish automated and open source algorithms for quality assurance and analysis, 3) partner with Imaging Biometrics to develop a commercially integrated, vendor neutral image analysis platform for analyzing SAGE DSC-MRI data and 4) validate SAGE DSC-MRI tools for predicting glioma response to bevacizumab therapy. Impact on Healthcare: We will provide the neuro-oncology community with validated, quantitative image acquisition and analysis methods for identifying early therapeutic response that are appropriate for multi-site clinical trials of conventional and targeted brain tumor therapies, thereby enabling more rapid drug discovery and improved individualized care for patients.